Indirect CRISPR screening with photoconversion revealed key factors of drug resistance with cell–cell interactions

Comprehensive screenings to clarify indirect cell–cell interactions, such as those in the tumor microenvironment, especially comprehensive assessments of supporting cells’ effects, are challenging. Therefore, in this study, indirect CRISPR screening for drug resistance with cell–cell interactions was invented. The photoconvertible fluorescent protein Dendra2 was inducted to supporting cells and explored the drug resistance responsible factors of supporting cells with CRISPR screenings. Random mutated supporting cells co-cultured with leukemic cells induced drug resistance with cell–cell interactions. Supporting cells responsible for drug resistance were isolated with green-to-red photoconversion, and 39 candidate genes were identified. Knocking out C9orf89, MAGI2, MLPH, or RHBDD2 in supporting cells reduced the ratio of apoptosis of cancer cells. In addition, the low expression of RHBDD2 in supporting cells, specifically fibroblasts, of clinical pancreatic cancer showed a shortened prognosis, and a negative correlation with CXCL12 was observed. Indirect CRISPR screening was established to isolate the responsible elements of cell–cell interactions. This screening method could reveal unknown mechanisms in all kinds of cell–cell interactions by revealing live phenotype-inducible cells, and it could be a platform for discovering new targets of drugs for conventional chemotherapies.

This study introduces an interesting approach to investigate drug resistance, caused by supporting cells of the tumor microenvironment that are found in proximity/physical interaction with tumor cells. In their method the authors transduced HEK293 cells with a whole genome library of gdRNAs. The HEK293 cells were cocultured with a leukemia cell line in the presence of a drug in small wells, which were then selected for viable tumor cells. Cells were collected and plated for expansion. After expansion, the HEK293 cells in the same location with drug-resistant (live) tumor cells were marked, using the Dendra2 protein that changes color from green to red, once exposed to UV light. The HEK293 cells were then sorted for red fluorescence. The authors identified a set of 39 candidates, and generated single knockouts per candidate, testing them in coculture with tumor cells. 11 of these single knockouts generated higher number of viable cells compared with non-targeting knockouts in both HEK293 and UE7T. These 11 knockouts were further tested in coculture with pancreatic cell lines treated with a drug and tested for viability compared with non-targeting knockouts and for cleaved caspase 3 activity in HEK293-U937 cocultures. These results narrowed down the list of candidate knockouts to 4. HEK293 with these knockouts were then profiled by RNAseq to identify possible mechanism for resistance which suggested CXCL12 as a possible regulator of drug resistance through the PI3K-AKT-mTOR pathway. The method presented by the authors is unique and novel, however, the CRISPR data provided by the authors is lacking and the data supporting the suggested mechanism is not strong enough. 1. The coverage of gdRNA to cells is on the lower side of ~100 fold. 2. There are no files in the results for the sequencing of the different gdRNAs, and there are no graphs that show the distribution of the guides before and after coculture. It is unclear how many guides showed depletion, enrichment after treatment. Also, there is no comparison between the two replicates and how much overlap there is between them. 3. How many of the 39 genes have no expression HEK cells? 4. The authors mention that they sorted 1000 genes from each 6 well, it is unclear why they chose such low number of cells, considering the huge loss of cells during sorting. 5. 5. The authors suggest that drug resistance happens through cytokine production of CXCL12 but the supporting data for that is not convincing enough. What are the CXCL12 levels secreted by the different knockouts in HEK193, UE7T? Does coculture of HEK293, UE7T with CXCL12 recombinant protein generates the same observed drug resistance of the tumor cells? can CXCL12 on its own generate drug resistance in tumor cells? How do the authors explain the absence of CXCL12 from the list of knockout candidates? Would CXCL12 knockout in HEK293, UE7T generate the same results? 6. Do the different knockouts in HEK293 also show resistance to higher doses of the drug in monocultures without tumors? 7. What do the x-axis in figure 4f represent? Please provide the scatter plots by flow. What is the gating strategy? 8. Please provide a tab delimited summary file for the gene level RNAseq data 9. Please provide cell numbers for figures 2b,c, 3 Reviewer #2 (Remarks to the Author): The manuscript by Sugita and coauthors reports an indirect CRISPR screening system with photoconvertible fluorescent protein Dendra2 to screen for key factors responsible for drug resistance with cell-cell interactions. The authors first applied this indirect CRISPR screening approach in HEK293T-U937 co-culture system and found 39 candidate genes with drug resistance functions in supporting cells. Then they validated these candidate genes in several different co-culture systems, investigated their biological features, and found a new axis linking RHBDD2, CXCL12, and PI3k-Akt-mTOR which is responsible for anticancer drug resistance. Finally, they analyzed the expression level of four important candidate genes in clinical pancreatic cancer samples to show their significance as prognostic factors. Overall, this manuscript is well-written with clear rationale and detailed methods for reproducing the work. The indirect CRISPR screening system is novel with good potential to be applied for screening in other cell-cell interaction systems. The discovery of RHBDD2-CXCL12 axis is also new and interesting. The reviewer only has several concerns. Concerns: 1. In Fig. 2 Figure 3, the viability of U937 cells in the co-culture system was assessed by quantifying GFP-positive cells as viable U937 cells. The reviewer is concerned if the GFPpositive cells can correctly represent viable cells. 2. There are 39 candidate genes identified by screening. However, only 11 genes were successfully validated with drug resistance functions (Fig. 2). How do you explain why the other 28 candidate genes were identified by screening but not successfully validated? Does that mean the indirect CRISPR screening assay has a high false positive ratio? 3. There is no clear description and citation of the CRISPR knockout library used for the indirect CRISPR screening. Please add this information in the manuscript. 4. For line 120-121, please describe how the conditions for drug selection was tightened. 5. Please include a calibration bar in Supplementary Figure 1 to show the relative intensity of the red channel. 6. It's better to include a diagram to describe the newly discovered axis responsible for anticancer drug resistance in the main figure to help the readers to understand. 7. For line 103, please include a comma after "As we expected".

and Supplementary
We would like to thank the reviewers for their thoughtful and thorough analysis of our manuscript.
We have endeavored to address all of the issues they presented. The response comments are in bold and blue. This study introduces an interesting approach to investigate drug resistance, caused by supporting cells of the tumor microenvironment that are found in proximity/physical interaction with tumor cells.
In their method the authors transduced HEK293 cells with a whole genome library of gdRNAs. The HEK293 cells were cocultured with a leukemia cell line in the presence of a drug in small wells, which were then selected for viable tumor cells. Cells were collected and plated for expansion. After expansion, the HEK293 cells in the same location with drug-resistant (live) tumor cells were marked, using the Dendra2 protein that changes color from green to red, once exposed to UV light. The HEK293 cells were then sorted for red fluorescence. The authors identified a set of 39 candidates, and generated single knockouts per candidate, testing them in coculture with tumor cells. 11 of these single knockouts generated higher number of viable cells compared with non-targeting knockouts in both HEK293 and UE7T. These 11 knockouts were further tested in coculture with pancreatic cell lines treated with a drug and tested for viability compared with non-targeting knockouts and for cleaved caspase 3 activity in HEK293-U937 cocultures. These results narrowed down the list of candidate knockouts to 4 HEK293 with these knockouts were then profiled by RNAseq to identify possible mechanism for resistance which suggested CXCL12 as a possible regulator of drug resistance through the PI3K-AKT-mTOR pathway.
The method presented by the authors is unique and novel, however, the CRISPR data provided by the authors is lacking and the data supporting the suggested mechanism is not strong enough. Response: Thank you for the important insights. The screening methods developed in the present study were based on microscope observation and manual photoconversion, and we recognized in the pre-screening that there was a numerical limit to the number of cells that could be labeled and collected. Therefore, we did not analyze gRNAs of the co-cultured cells using NGS and, unfortunately, we did not store those samples suitably for DNA extraction of co-cultured cells as part of the present study. Therefore, we could not perform sequencing of the different gRNAs before and after coculturing.
There are zero overlaps in the identified genes between the two independent replicates (Table 1); this may be because the number of candidate genes is small in the present study.
The present study aimed to develop indirect screening methods using photoconversion to clarify indirect cell-cell interaction mechanisms. Fortunately, positive candidates were obtained from the screening. We validated their ability to induce drug resistance and analyzed their biological function. We recognize that the most critical limitation of this research is the numerical limitation caused by microscopic observation and manual illumination with labeling and separating of target cells. We would like to develop a high-throughput screening system that automatically recognizes the target cells, automatically illuminates them with a laser, and 1. The coverage of gdRNA to cells is on the lower side of ~100 fold.
2. There are no files in the results for the sequencing of the different gdRNAs, and there are no graphs that show the distribution of the guides before and after coculture. It is unclear how many guides showed depletion, enrichment after treatment. Also, there is no comparison between the two replicates and how much overlap there is between them.

separates all of them, thereby enabling us to process a large number of cells without losing any candidates.
We are planning to develop such a high-throughput screening system, confirm the diversity of all cells during screening, collect all labeled cells, and analyze those collected cells using NGS. By applying such improved methods, we hope to elucidate mechanisms of cell-cell interactions more efficiently. 3. How many of the 39 genes have no expression HEK cells?

Response
Response: Yes, we agree this point. The scenario stands as a technical limitation of the present study. In the present screening system, there were live U937 colonies that were observed under the microscope to be "mulberry-like" under cytarabine exposure.
During the present drug selection, mulberry-like U937 cell colonies were identified and the drug-resistance-inducible HEK293T cells also proliferated continuously at the bottom of the colonies. In that scenario, we deemed that collecting all drug-resistance-inducible cells was not necessary, with it instead appropriate to sample just a portion of drug-resistance-inducible HEK293T cells in the present study with technical limitations. The 1000 sorted cells might provide coverage for the drug-resistance-inducible -phenotype cells we sought to collect.
We manually photoconverted as many of these viable colonies as possible using laser illumination. We will sort the photoconverted red form of Dendra2 more efficiently with automatic illuminating systems, meaning it will be possible to sort a greater amount in the future.
4. The authors mention that they sorted 1000 genes from each 6 well, it is unclear why they chose such low number of cells, considering the huge loss of cells during sorting.   Fig. 4f, g).

Co-culture experiments of parental HEK293T-U937 cells and parental UE7T-9-U937 cells with
cytarabine exposure after pre-treatment with the recombinant CXCL12 were also performed.
The results showed that cytarabine exposure following the treatment with recombinant

CXCL12 decreased cleaved caspase-3-positive cells under cytarabine exposure, which was similar to the effect observed when the cells were co-cultured with HEK293T RHBDD2-CKO
or UE7T-9 RHBDD2-CKO ( Supplementary Fig. 11a, b). In addition, in the U937 mono-culture experiment, cleaved caspase-3-positive cells under cytarabine exposure were slightly but significantly decreased (Supplementary Fig. 11c). These results suggest that a high concentration of CXCL12 secreted from the supporting cells might induce cytarabine resistance in U937.

pre-treatment with CXCL12-neutralizing antibodies increased cleaved caspase-3-positive cells under cytarabine exposure for 48 h, which was similar to the effect observed when the cells
were co-cultured with control HEK293T or UE7T-9. j, k Evaluation of phospho-Akt downstream of CXCL12 in GFP-positive U937 under mono-or co-culture conditions. The revised part is highlighted in the manuscript as follows:
Response: Thank you for your consideration. There could be many reasons why some candidate genes were identified by screening but not successfully validated. Generally, this will have been caused by the technical limitations of random screening and the original systems used in the present study.
2. There are 39 candidate genes identified by screening. However, only 11 genes were successfully validated with drug resistance functions (Fig. 2). How do you explain why the other 28 candidate genes were identified by screening but not successfully validated?
Does that mean the indirect CRISPR screening assay has a high false positive ratio?
In the screening experiment, HEK293T cells, in close proximity to viable colonies identified under microscopic observation, were manually illuminated by the laser. We speculate that the false-positive rate in labeling candidate cells was caused by the inevitable exposure of the laser beam to non-objective cells surrounding the objective candidate cells.
Additionally, in the co-culture validation experiments, the concentration of cytarabine was 5 μM, higher than the concentration used in screening, 3 μM, to select candidate genes of stronger phenotypes inducing anticancer drug resistance.
Of course, off-target effects of gRNAs were also potentially responsible for false-positive candidates.
To overcome the problem of false positives, repeated validation experiments were conducted using multiple cell types in the present study. We are currently considering methods to improve the system to isolate objective candidate cells accurately and efficiently.
Response: Thank you for pointing out this missing information. It was provided in Supplementary Table 1  4. For line 120-121, please describe how the conditions for drug selection was tightened.

Results (Line 119-121)
Collected mutated HEK293T cells were then expanded and the mutation was analyzed using TA cloning, after which 39 candidate genes were obtained (Fig. 1b, Step 7, Table 1). Response: We appreciate this comment. Figure 5d was inserted to explain Line 308-310 in the Discussion and demonstrate the newly discovered axis.

Figure 5d
5. Please include a calibration bar in Supplementary Figure 1 to show the relative intensity of the red channel.
6. It's better to include a diagram to describe the newly discovered axis responsible for anticancer drug resistance in the main figure to help the readers to understand.

Manuscript ID: COMMSBIO-22-3524A
Indirect CRISPR screening with photoconversion revealed key factors of drug resistance with cellcell interactions We would like to thank the reviewers for their thoughtful and thorough analysis of our manuscript.
We have endeavored to address all of the issues they presented. The response comments are in bold and blue.

REVIEWERS' COMMENTS:
Reviewer #1 (Remarks to the Author): I believe the authors addressed all my comments and I think the paper should be accepted.

Thank you for your time and valuable feedback.
Reviewer #2 (Remarks to the Author): The authors addressed most of the reviewer's concerns properly. The reviewer only has two suggestions here.
Thank you for your thoughtful analysis. We also thank you generally for your time and valuable feedback.
1. For concern 2, it's better to include the discussion about why many candidate genes identified by screening were not successfully validated into the discussion part of the manuscript. It helps the readers to understand the reported method thoroughly and also pay attention to this limitation when they try to apply this method.
We appreciate this comment. We have added the following text in the discussion part of the manuscript.

Discussion (Line 298-312)
In the present study, there are 39 candidate genes identified by screening. However, only 11 genes were successfully validated with drug resistance functions. There could be many reasons why some candidate genes were identified by screening but not successfully validated.
Generally, this will have been caused by the technical limitations of random screening and the original systems used in the present study. In the screening experiment, HEK293T cells, in close proximity to viable colonies identified under microscopic observation, were manually illuminated by the laser. We speculate that the false-positive rate in labeling candidate cells was caused by the inevitable exposure of the laser beam to non-objective cells surrounding the objective candidate cells. Additionally, in the co-culture validation experiments, the concentration of cytarabine was 5 μM, higher than the concentration used in screening, 3 μM, to select candidate genes of stronger phenotypes inducing anticancer drug resistance. Of course, off-target effects of gRNAs were also potentially responsible for false-positive candidates. To overcome the problem of false positives, repeated validation experiments were conducted using multiple cell types in the present study. We are currently considering methods to improve the system to isolate objective candidate cells accurately and efficiently.
2. For concern 5, the reviewer means to add a calibration bar to each of the two red channels.
Otherwise, it's hard to compare the intensity of the red channel before and after 405 nm laser illumination.
Thank you for pointing out. We add a calibration bar to each of the two red channels, before and after photoconversion, to Supplementary Figure 1.

Supplementary Figure 1.
We believe we have successfully addressed all of the reviewers' concerns.
Thank you again for your time and valuable feedback.